Method and electronic device for predicting response

ABSTRACT

An electronic device and a method for predicting a response are provided. The electronic device includes a display and a processor configured to receive at least one message, identify at least one contextual category of the at least one message, predict at least one response for the at least one message from a language model based on the at least one contextual category, and control the display to display the at least one predicted response.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the benefit under 35 U.S.C. §119(a) of IndianProvisional Patent Application No. 201641019244 filed on Jun. 2, 2016 inthe Indian Intellectual Property Office, and Indian Patent ApplicationNo. 201641019244 filed on Jun. 1, 2017 in the Indian IntellectualProperty Office, the entire disclosure of each of which is herebyincorporated by reference.

TECHNICAL FIELD

The present disclosure relates to electronic devices. More particularly,the present disclosure relates to a method and an electronic device forpredicting response.

BACKGROUND

In general, a word prediction technique involves an n-gram languagemodel. The goal of the n-gram language model is to compute probabilityof a sentence or sequence of words and use it to compute the probabilityof a suggesting word (i.e., upcoming word). Typically bigram or trigrammodels are used considering the speed and size of language models. Thetrigram model includes unigram, bigram and trigram features. The unigramfeature is based on the current word being typed, the feature bigram isbased on the current word being typed and the previous word that istyped, and the trigram feature is based on the current word being typedand the previous two words that are typed. However, these models havelong-distance dependencies. Hence, it is difficult to track longersentences and predictions may not be accurate.

If the n-gram language model is extended to 7-grams (for example), itmay be able to the track previous 7 words and the longer sentence. Thiscan lead to exponential growth of the number of parameters with lengthof the n-gram language model and hence the increase in complexity intraining the language model, increase in training time of the languagemodel, and performance expensive storage and retrieval operations inexisting systems. Hence, it is not recommended to increase the size ofthe n-gram language model in the existing systems. Further, these modelsare based on the current sentence being typed. The intention behindtyping the sentence is ignored.

The above information is presented as background information only toassist with an understanding of the present disclosure. No determinationhas been made, and no assertion is made, as to whether any of the abovemight be applicable as prior art with regard to the present disclosure.

SUMMARY

Aspects of the present disclosure are to address at least theabove-mentioned problems and/or disadvantages and to provide at leastthe advantages described below. Accordingly, an aspect of the presentdisclosure is to provide a method and electronic device for predicting aresponse.

Another object of the embodiments herein is to provide a method forreceiving at least one message, and identifying at least one contextualcategory of the at least one message.

Another object of the embodiments herein is to provide a method forpredicting at least one response for the at least one message from alanguage model (e.g., local language model) based on the at least onecontextual category.

Another object of the embodiments herein is to provide a method forcausing to display the at least one predicted response on a screen ofthe electronic device.

Yet another object of the embodiments herein is to provide a method forreceiving an input topic from a first application and identifying atleast one contextual event associated with a second application

Yet another object of the embodiments herein is to provide a method forpredicting at least one response for the at least one input topic fromthe first application based on at least one contextual event.

Yet another object of the embodiments herein is to provide a method forcausing to display the at least one predicted response on a screen ofthe electronic device.

Yet another object of the embodiments herein is to provide a method forreceiving an input topic and identifying at least one contextualcategory of the input topic.

Yet another object of the embodiments herein is to provide a method forpredicting at least one response for the input topic from a languagemodel based on the at least one contextual category, and causing todisplay the at least one predicted response on a screen of theelectronic device.

In accordance with an aspect of the present disclosure, a method forpredicting response at an electronic device is provided. In accordancewith another aspect of the present disclosure, a controlling method ofan electronic apparatus is provided. The method includes receiving atleast one message at the electronic device. Further, the method includesidentifying at least one contextual category of the at least onemessage. Further, the method includes predicting at least one responsefor the at least one message from a language model based on the at leastone contextual category. Furthermore, the method includes causing todisplay the at least one predicted response on a screen of theelectronic device.

In an embodiment, the contextual category of the at least one message isautomatically identified based on at least one context indicative.

In an embodiment, the at least one context indicative is dynamicallydetermined based on at least one of content available in the at leastone message, user activities, events defined in the electronic device, auser associated with the at least one message, a user context, and acontext of the electronic device.

In an embodiment, the at least one message is displayed within anotification area of the electronic device.

In an embodiment, the at least one predicted response is displayedwithin the notification area of the electronic device.

In an embodiment, the at least one response for the at least one messageis predicted in response to an input on the at least one receivedmessage, wherein the at least one predicted response corresponds to theat least one received message on which the input is received.

Accordingly embodiments herein provide a method for predicting aresponse at an electronic device. The method includes receiving an inputtopic from a first application. Further, the method includes identifyingat least one contextual event associated with a second application.Further, the method includes predicting at least one response for the atleast one input topic from the first application based on at least onecontextual event. Furthermore, the method includes causing to displaythe at least one predicted response on a screen of the electronicdevice.

In an embodiment, the at least one contextual event is a time boundevent.

In an embodiment, the at least one contextual event associated with thesecond application is dynamically determined based on at least onecontext indicative associated with the input topic of the firstapplication.

In an embodiment, the at least one context indicative is determinedbased on at least one of content available in the input topic, contextof the first application, user activities, and events defined in theelectronic device.

Accordingly embodiments herein provide a method for predicting aresponse at an electronic device. The method includes receiving an inputtopic. Further, the method includes identifying at least one contextualcategory of the input topic. Further, the method includes predicting atleast one response for the input topic from a language model based onthe at least one contextual category. Furthermore, the method includescausing to display the at least one predicted response on a screen ofthe electronic device.

In an embodiment, the input topic is one of a topic selected from awritten communication, and a topic formed based at least one input filedavailable in an application.

In an embodiment, the at least one contextual category of the inputtopic is automatically identified based on at least one contextindicative.

In an embodiment, the at least one context indicative is dynamicallydetermined based on at least one of content available associated withthe input topic, user activities, events defined in the electronicdevice, and a user context and a context of the electronic device.

Accordingly embodiments herein provide an electronic device forpredicting a response. The electronic device includes a contextidentifier configured to receive at least one message. Further, theelectronic device includes a contextual category detector configured toidentify at least one contextual category of the at least one message.Furthermore, the electronic device includes a response predictorconfigured to: predict at least one response for the at least onemessage from a language model based on the at least one contextualcategory, and cause to display the at least one predicted response on ascreen.

Accordingly embodiments herein provide an electronic device forpredicting a response. The electronic device includes a contextidentifier configured to receive an input topic from a firstapplication. Further, the electronic device includes a contextualcategory detector configured to identify at least one contextual eventassociated with a second application. Furthermore, the electronic deviceincludes a response predictor configured to: predict at least oneresponse for the at least one input topic from the first applicationbased on at least one contextual event, and cause to display the atleast one predicted response on a screen of the electronic device.

Accordingly embodiments herein provide an electronic device forpredicting a response. The electronic device includes a contextidentifier configured to receive an input topic. Further, the electronicdevice includes a contextual category detector configured to identify atleast one contextual category of the input topic. Furthermore, theelectronic device includes a response predictor configured to: predictat least one response for the input topic from a language model based onthe at least one contextual category, and cause to display the at leastone predicted response on a screen.

Other aspects, advantages, and salient features of the disclosure willbecome apparent to those skilled in the art from the following detaileddescription, which, taken in conjunction with the annexed drawings,discloses various embodiments of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of certainembodiments of the present disclosure will be more apparent from thefollowing description taken in conjunction with the accompanyingdrawings, in which:

FIGS. 1A to 1D illustrate various types of N-Gram language modelsaccording to the related art;

FIG. 2 illustrate a User Interface (UI) for responding to the messageusing at least one predicted response, according to the related art;

FIG. 3A illustrates a schematic view of (N+X) gram language model/(NN+X)language model, according to an embodiment of the present disclosure;

FIG. 3B illustrate the UI for responding to the message using at leastone predicted response, according to an embodiment of the presentdisclosure;

FIG. 4 is a block diagram illustrating various hardware elements of anelectronic device, according to an embodiment of the present disclosure;

FIG. 5 is an overview illustrating communication among various hardwareelements of the electronic device for automatically predicting theresponse, according to an embodiment of the present disclosure;

FIGS. 6A and 6B illustrate a UI for predicting subsequent meaningfulprediction during composing of a response to the received message,according to embodiments disclosed herein;

FIG. 7 is a flow diagram illustrating a method for predicting theresponse, according to an embodiment of the present disclosure;

FIG. 8 illustrates a UI for responding to the message using the at leastone predicted response, according to an embodiment of the presentdisclosure;

FIG. 9A is a step by step illustration for predicting response for aselected message from a plurality of messages, according to anembodiment of the present disclosure;

FIG. 9B is a step by step illustration for predicting response based fora selected input topic, according to an embodiment of the presentdisclosure;

FIG. 10 is a flow diagram illustrating a method for predicting theresponse based on the statistical modelling manager, accordingembodiments as disclosed herein;

FIG. 11 is a graph illustrating of computing dynamic interpolationweights with time bound, according to an embodiment of the presentdisclosure;

FIGS. 12A and 12B illustrate a UI in which the contextual event from thereceived message is identified and extended from first application tosecond application, according to an embodiment of the presentdisclosure;

FIGS. 13A and 13B illustrate another UI in which the contextual eventfrom the received message is identified and extended from firstapplication to second application, according to an embodiment of thepresent disclosure;

FIG. 14A illustrates a UI in which a contextual related applicationbased on the received message is predicted and displayed on the screenof the electronic device, according to an embodiment of the presentdisclosure;

FIG. 14B illustrates a UI in which the predicted response for themessage is displayed with in the notification area of the electronicdevice, according to an embodiment of the present disclosure;

FIG. 15 illustrates a UI in which multiple response messages arepredicted based on contextual grouping of the related messages,according to an embodiment of the present disclosure;

FIGS. 16A and 16B illustrate longer pattern scenarios in which themeaningful response (i.e., next suggestion word) is predicted in alonger pattern sentence, according to an embodiment of the presentdisclosure;

FIG. 17 is a flow diagram illustrating a method for predicting theresponse by understanding input views rendered on the screen of theelectronic device, according to an embodiment of the present disclosure;

FIGS. 18A to 18C illustrate the UI displaying at least one predictedresponse by understanding input views rendered on the screen of theelectronic device, according to an embodiment of the present disclosure;

FIGS. 19A to 19C illustrate the UI displaying multiple predictedresponse based on at least one event associated with at least oneparticipant, according to an embodiment of the present disclosure;

FIGS. 20A to 20C illustrate the UI displaying predicted response basedon the context associated with the user and the electronic device,according to an embodiment of the present disclosure; and

FIGS. 21A to 21D illustrates various table tabulating the responsepredictions and next suggestive words for different samples of inputs,according to an embodiment of the present disclosure.

Throughout the drawings, it should be noted that like reference numbersare used to depict the same or similar elements, features, andstructures.

DETAILED DESCRIPTION

The following description with reference to the accompanying drawings isprovided to assist in a comprehensive understanding of variousembodiments of the present disclosure as defined by the claims and theirequivalents. It includes various specific details to assist in thatunderstanding but these are to be regarded as merely exemplary.Accordingly, those of ordinary skill in the art will recognize thatvarious changes and modifications of the various embodiments describedherein can be made without departing from the scope and spirit of thepresent disclosure. In addition, descriptions of well-known functionsand constructions may be omitted for clarity and conciseness.

The terms and words used in the following description and claims are notlimited to the bibliographical meanings, but, are merely used by theinventor to enable a clear and consistent understanding of the presentdisclosure. Accordingly, it should be apparent to those skilled in theart that the following description of various embodiments of the presentdisclosure is provided for illustration purpose only and not for thepurpose of limiting the present disclosure as defined by the appendedclaims and their equivalents.

It is to be understood that the singular forms “a,” “an,” and “the”include plural referents unless the context clearly dictates otherwise.Thus, for example, reference to “a component surface” includes referenceto one or more of such surfaces.

By the term “substantially” it is meant that the recited characteristic,parameter, or value need not be achieved exactly, but that deviations orvariations, including for example, tolerances, measurement error,measurement accuracy limitations and other factors known to those ofskill in the art, may occur in amounts that do not preclude the effectthe characteristic was intended to provide.

Various embodiments of the present disclosure described herein are notnecessarily mutually exclusive, as some embodiments can be combined withone or more other embodiments to form new embodiments.

Herein, the term “or” as used herein, refers to a non-exclusive or,unless otherwise indicated. The examples used herein are intended merelyto facilitate an understanding of ways in which the various embodimentsherein can be practiced and to further enable those skilled in the artto practice the various embodiments herein. Accordingly, the examplesshould not be construed as limiting the scope of the various embodimentsherein.

As is traditional in the field, various embodiments may be described andillustrated in terms of blocks which carry out a described function orfunctions. These blocks, which may be referred to herein as units ormodules or the like, are physically implemented by analog and/or digitalcircuits, such as logic gates, integrated circuits, microprocessors,microcontrollers, memory circuits, passive electronic components, activeelectronic components, optical components, hardwired circuits and thelike, and may optionally be driven by firmware and/or software. Thecircuits may, for example, be embodied in one or more semiconductorchips, or on substrate supports, such as printed circuit boards and thelike. The circuits constituting a block may be implemented by dedicatedhardware, or by a processor (e.g., one or more programmedmicroprocessors and associated circuitry), or by a combination ofdedicated hardware to perform some functions of the block and aprocessor to perform other functions of the block. Each block of thevarious embodiments may be physically separated into two or moreinteracting and discrete blocks without departing from the scope of thedisclosure. Likewise, the blocks of the various embodiments may bephysically combined into more complex blocks without departing from thescope of the disclosure.

Accordingly embodiments herein provide a method for predicting responseat an electronic device. The method includes receiving at least onemessage at the electronic device. Further, the method includesidentifying at least one contextual category of the at least onemessage. Further, the method includes predicting at least one responsefor the at least one message from a language model based on the at leastone contextual category. Furthermore, the method includes causing todisplay the at least one predicted response on a screen of theelectronic device.

In the related art, the predictions of word(s), graphical element, orthe like are determined and updated only during typing. For example,consider a scenario in which a user of the electronic device receives amessage from a “X” source. Once, the electronic device detects an inputin the event of responding to the message, a keyboard may automaticallylaunch aiding the user in composing the response to the message.Further, as the user starts composing the response for the message, theexisting systems may determine the text/word during typing and predictthe related text/words, graphical element, or the like. Thus, thepredicted words are determined and updated only during typing. Unlikethe conventional methods and systems, the proposed method can be used toprovide the prediction of words even before the user starts composingthe response to the message.

Generally, only default word(s)/text/graphical element, or the like areprovided to the user of the electronic device prior to composing theresponse to the received message. Yet, again the default words are notmeaningful (or, unrelated) to the context of the conversation/ to thereceived message. According to various embodiments of the presentdisclosure, the proposed method can be used to predict the words whichare relevant to the context of the conversation/ to the receivedmessage. Thus, improving the user experience by providing the relevantand related graphical elements while composing.

Accordingly, the proposed method of the present disclosure can be usedto provide predictions for longer sentences or providing predictions forthe sentences being typed. The proposed method can be used to performthe word prediction based on the contextual input class.

In an embodiment of the present disclosure, when the user has to respondmultiple messages, upon selecting a thread, relevant predictions shallbe provided. In other aspect, message or thread selection can be doneautomatically by using.

FIGS. 1A to 1D illustrate various types of N-Gram language models,according to the related art.

Referring to the FIG. 1A, the N-Gram language model is driven fromequation-1 (shown below). The N-Gram language model includes a generaln-gram language model that predicts/suggests current word based on theprevious set of words. Thus, the N-Gram language model can determine theset of words associated with input-1 and input-2, and provides theprediction/suggestions based on the determined set of words associatedwith the input-1 and the input-2.

$\begin{matrix}{{P\left( {w_{1},\ldots \;,w_{m}} \right)} = {{\prod\limits_{i = 1}^{m}\; {P\left( {\left. w_{i} \middle| w_{1} \right.,\ldots \;,w_{i - 1}} \right)}} \approx {\prod\limits_{i = 1}^{m}\; {P\left( {\left. w_{i} \middle| w_{i - {({n - 1})}} \right.,\ldots \;,w_{i - 1}} \right)}}}} & (1)\end{matrix}$

For example, if the electronic device 100 detects the input-1 as“Friday” and the further detects the input-2 as “night”, then thepredicted/suggested words can include, for example, “is”, “are”, “so”,or the like, which are not meaningful.

Referring to FIG. 1B, the N-Gram language model for a class is drivenfrom equation-2 (shown below). The N-Gram Model includespredicting/suggesting the current word based on previous set of wordsand their respective classes.

p(w _(i−k) w _(i−k+1) . . . w _(i−1))

p(c _(i) |c _(i−k) c _(i−k+1) . . . c _(i−1))p(w _(i) |c _(i)),   (2)

Referring to FIG. 1C, the N-Gram language model for a phrase is drivenfrom equation-3 (shown below). The N-Gram language model includespredicting one or more words (phrase) based on the previous set ofwords.

$\begin{matrix}{{p(w)}\overset{def}{=}{{p(\gamma)}.\begin{matrix}{{p(\gamma)} = {p\left( {\gamma_{1}\gamma_{2}\ldots \; \gamma_{m}^{\prime}} \right)}} \\{{= {\prod\limits_{i = 1}^{m^{\prime}}\; {p\left( \gamma_{i} \middle| {\gamma_{1}\ldots \; \gamma_{i - 1}} \right)}}},} \\{{{p\left( \gamma_{i} \middle| {\gamma_{1}\ldots \; \gamma_{i - 1}} \right)} \approx {p\left( \gamma_{i} \middle| {\gamma_{i - k}\gamma_{i - k + 1}\ldots \; \gamma_{i - 1}} \right)}},}\end{matrix}}} & (3)\end{matrix}$

Referring to FIG. 1D, the N-Gram language model for a phrase class isdriven from equation-4 (shown below). The N-Gram language model includespredicting one or more words (phrase) based on the previous set of wordsand their respective classes.

$\begin{matrix}\begin{matrix}{{p(w)}\overset{{def} \cdot}{=}{p(\gamma)}} \\{= {p\left( {\gamma_{1}\gamma_{2}\ldots \; \gamma_{m}^{\prime}} \right)}} \\{{= {\prod\limits_{i = 1}^{m^{\prime}}\; {p\left( \gamma_{i} \middle| {\gamma_{1}\gamma_{2}\ldots \; \gamma_{i - 1}} \right)}}},} \\{{{p\left( \gamma_{i} \middle| {\gamma_{1}\gamma_{2}\ldots \; \gamma_{i - 1}} \right)} \approx {p\left( \gamma_{i} \middle| {\gamma_{i - k}\gamma_{i - k + 1}\ldots \; \gamma_{i - 1}} \right)}},} \\{{{p\left( \gamma_{i} \middle| {\gamma_{i - k}\gamma_{i - k + 1}\ldots \; \gamma_{i - 1}} \right)}\overset{{def} \cdot}{=}{{p\left( c_{i} \middle| {c_{i - k}c_{i - k + 1}\ldots \; c_{i - 1}} \right)}{p\left( \gamma_{i} \middle| c_{i} \right)}}},}\end{matrix} & (4)\end{matrix}$

FIG. 2 illustrate a User Interface (UI) for responding to the messageusing at least one predicted response, according to the related art.

Referring to FIG. 2, the electronic device 100 may have a messagetranscript 200 showing a conversation between the user of the electronicdevice 100 and one or more participants, such as participant 204. Themessage transcript 200 may include a message 202 received from (anelectronic device used by) the participant 204.

The content of the message 202 “Hey I got my results. Looks like I amthe topper!:D” is conveying the happiness from the participant 204 tothe user of the electronic device 100. If the user of the electronicdevice 100 may intent to respond to the message 202, according to theexisting mechanisms, only default graphical element 206 i.e., “Ok”, “I”,or the like., are predicted and displayed on the screen of theelectronic device 100. Alternately, the default graphical element 206may prone to change (i.e., update) as the user starts typing (i.e.,responding) to the message 202.

Further, in regards to the user typing i.e., if the n-gram (or, neuralnet (NN)) is extended to 7-grams (for example), it may be able to trackprevious 7 words and the longer sentence. This can lead to exponentialgrowth of the number of parameters with length of the n-gram languagemodel (or, NN language model) and hence the increase in complexity intraining the language model (N/n gram, NN gram), increase in trainingtime of the language model, and performance expensive storage andretrieval operations in the existing systems.

FIG. 3A illustrates a schematic view of a (N+X) gram languagemodel/(NN+X) language model, according to an embodiment of the presentdisclosure.

In an embodiment of the present disclosure, the electronic device 100can utilize the contextual category of the input(s) i.e., input-1 andinput-2, along with the bigram or trigram features of the input(s), thiscan be derived using equations (5) and (6).

$\begin{matrix}\begin{matrix}{{p(w)}\overset{{def} \cdot}{=}{p(\gamma)}} \\{= {p\left( {\gamma_{1}\gamma_{2}\ldots \; \gamma_{m}^{\prime}} \right)}} \\{{= {\prod\limits_{i = 1}^{m^{\prime}}\; {p\left( \gamma_{i} \middle| {\gamma_{1}\gamma_{2}\ldots \; \gamma_{i - 1}} \right)}}},} \\{{p\left( \gamma_{i} \middle| {\gamma_{1}\gamma_{2}\ldots \; \gamma_{i - 1}} \right)} \approx {p{\left( \gamma_{i} \middle| {\gamma_{i - k}\gamma_{i - k + 1}\ldots \; \gamma_{i - 1}} \right).}}}\end{matrix} & (5)\end{matrix}$p(γ_(i)|γ_(i−k)γ_(i−k+1) . . . γ_(i−1))

p(c _(i) |c _(i−k) c _(i−k+1) . . . c _(i−1) , c _(Input1) , c _(Input2), . . . , c _(InputN))p(γ_(i) |c _(i))   (6)

For example, contextual category of the input(s) can be identified byparsing the screen i.e., parts of speech associated with the contentsavailable on the screen, sentence classification, dependency parser, orthe like.

FIG. 3B illustrates the UI for responding to the message using at leastone predicted response, according to an embodiment of the presentdisclosure.

In an embodiment of the present disclosure, the electronic device 100can provide the meaningful predictions before the user of the electronicdevice 100 starts responding (i.e., typing) to the message. Themeaningful predictions are based on the context indicative of thereceived message. Further, the proposed method can be used to provide atleast one predicted response from a language model based on the at leastone contextual category of the message. Thus, the language model (e.g.,“N” gram language model/NN language model) utilizes the contextualcategory (“X”) of the message (e.g., (N+X)/ (NN+X)) as illustrated inFIG. 3A. Thus, reducing the complexity in training the language model,reducing the training time of the language model, and reducing theperformance expensive storage and retrieval operations.

Referring to FIG. 3B, the electronic device 100 may have a messagetranscript 300 showing a conversation between the user of the electronicdevice 100 and one or more participants, such as participant 304. Themessage transcript 300 may include a message 302, received from (anelectronic device used by) the participant 304.

The content of the message 302 “Hey I got my results. Looks like I amthe topper!:D” is conveying the happiness from the participant 304 tothe user of the electronic device 100. Unlike to conventional methodsand systems, the proposed method can be used to determine at least onecontextual category of the message 302 i.e., the contextual category ofthe received message 302 can be for example, “Appreciation”. Further,the proposed method can be used to predict at least one response 306i.e., “Wow”, “congrats”, “Awesome”, or the like from the language modelbased on the at least one contextual category.

In an embodiment of the present disclosure, the proposed method can beused to predict and display the at least one response, even before theuser starts composing the response message. Thus, improving the userexperience by displaying the meaningful predictions to the message 302.

FIG. 4 is a block diagram illustrating various hardware elements of theelectronic device, according to an embodiment of the present disclosure.

Referring to FIG. 4, the electronic device 100 can include, for example,a mobile phone, a smart phone, Personal Digital Assistants (PDAs), atablet, a wearable device, a computer, a laptop, etc. In an embodimentof the present disclosure, the electronic device 100 can include adisplay and a touch-sensitive surface.

The electronic device 100 may support a variety of applications, suchas, a messaging applications, a calendar application, a browserapplication, a word processing application, a telephone application, ane-mail application, an instant messaging application, a Short MessageService (SMS) message, a Multimedia Message Service (MMS) message, orthe like. Further, the variety of applications may optionally require atleast one of keypad, keyboard, touch sensitive surface, or the like, forinteracting with at least one feature of the at least one application.For example, add reminder is a feature of a calendar application,message composing is a feature of the messaging application, or thelike.

The electronic device 100 may include a communicator 110, an informationmanager 120, a contextual category detector 130, and a responsepredictor 140. Further, the electronic device 100 may include aprocessor 160, (for example; a hardware unit, an apparatus, a CentralProcessing Unit (CPU), a Graphics Processing Unit (GPU), etc.,)communicatively coupled to a storage (memory) 150 (e.g., a volatilememory and/or a non-volatile memory). The storage 150 may includestorage locations configured to be addressable through the processor160. The information manager 120, the contextual category detector 130,and the response predictor 140 may be coupled with the processor 160.The information manager 120, the contextual category detector 130, andthe response predictor 140 may be implemented by the processor 160.

The storage 150 can be can be coupled (or, communicatively coupled) withthe processor 160, the communicator 110, the information manager 120,the contextual category detector 130, and the response predictor 140. Inanother embodiment, the storage 150 can be remotely located to that ofthe processor 160, the communicator 110, the information manager 120,the contextual category detector 130, and the response predictor 140.

Furthermore, the electronic device 100 includes a display 170 capable ofbeing utilized to display on the screen of the electronic device 100. Inan embodiment of the present disclosure, the display 170 can be, forexample, a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD),Organic Light-Emitting Diode (OLED), a Light-emitting diode (LED),Electroluminescent Displays (ELDs), field emission display (FED), LPD(light emitting polymer display), etc. The display 170 can be configuredto display the one or more UI of the variety of applications. Thedisplay 170 can be coupled (or, communicatively coupled) with theprocessor 160 and the storage 150. Further, the display 170 can becoupled (or, communicatively coupled) with the information manager 120,the contextual category detector 130, and the response predictor 140.

The communicator 110 facilitates communication with other devices overone or more external ports (e.g., Universal Serial Bus (USB), FIREWIRE,etc.). The external port is adapted for coupling directly to otherelectronic devices or indirectly over a network (e.g., the Internet,wireless LAN, etc.). Further, the communicator 110 facilitatescommunication with the internal hardware elements of the electronicdevice 100.

The information manager 120, coupled with the communicator 110, can beconfigured to receive at least one message. The at least one message canbe at least one SMS message, SNS message, and the like that areassociated with at least one application from the aforementioned varietyof applications. The at least one message can include at least onecontent.

Further, the electronic device 100 can include a text input module (notshown) which may be a GUI component displayed on the screen of thedisplay 170. The GUI component can be, for example, virtual keypad,virtual keyboard, soft keyboards, and the like for entering the text inthe variety of applications.

In an embodiment of the present disclosure, the electronic device 100can include a Global positioning system (GPS) module (not shown) fordetermining the location of the electronic device 100 and provide thisinformation to the variety of applications running in the electronicdevice 100. Further, the electronic device 100 can include one or moresensors e.g., accelerometer sensor, proximity sensor, temperaturesensor, or the like. The electronic device 100 can be configured todetermine the context (e.g. weather related information, traffic relatedinformation, and the like) of the electronic device 100 using the one ormore sensors in combination with the GPS module.

Further, the contextual category detector 130 can be configured toidentify the at least one contextual category of the at least onemessage. In an embodiment of the present disclosure, the at least onecontextual category of the at least one message is automaticallyidentified based on at least one context indicative.

In an embodiment of the present disclosure, the at least one contextindicative is dynamically determined based on the content available inthe at least one message, user activities (e.g., user trackerinformation), events (e.g., time event, location event, relation event,and the like) defined in the electronic device 100, sensing data (e.g.,temperature, humidity, location, and the like) sensed by sensors of theelectronic device 100, received data from a server (e.g., weather, news,advertisement, and the like), a user (e.g., the participant) associatedwith the at least one message, a user context (e.g., appointment,health, user tone or the like), the context of the electronic device100, or the like.

The response predictor 140 can be configured to predict the at least oneresponse for the at least one message from the language model based onthe at least one contextual category. In an embodiment of the presentdisclosure, the language model can be, for example, N gram languagemodel. Based on the predicted response, the response predictor 140 canbe configured to display the at least one predicted response on thescreen of the display 170.

In another embodiment, the information manager 130 can be configured toreceive the input topic from a first application. The input topic canbe, for example, written text, at least one received message, or thelike. The first application can be any one of the application from theaforementioned variety of the applications.

Further, the contextual category detector 140 can be configured toidentify at least one contextual event associated with a secondapplication. In an embodiment of the present disclosure, the contextualevent can include, for example, the time event, the location event, therelation event, or the like. The second application can be any of theapplication from the aforementioned variety of the applications. In anembodiment of the present disclosure, the contextual event associatedwith the second application is dynamically determined based on at leastone context indicative associated with the input topic of the firstapplication.

In an embodiment of the present disclosure, the at least one contextindicative is determined based on, for example, content available in theinput topic, a context (weather application, calendar application,shopping application, etc.,) of the first application, the useractivities, and the events defined in the electronic device 100.

In yet another embodiment, the information manager 120 can be configuredto receive the input topic. The input topic can include, for example,topic selected from a written communication, topic formed based at leastone input filed available in the application, current editor, userselected content, text on screen of the electronic device 100, and thelike.

Further, the contextual category detector 130 can be configured toidentify at least one contextual category of the input topic. In anembodiment of the present disclosure, the at least one contextualcategory of the input topic is automatically identified based on atleast one context indicative. In an embodiment of the presentdisclosure, the at least one context indicative is dynamicallydetermined based on at least one of content available in the inputtopic, the user activities, the events defined in the electronic device100, the user context, and a context of the electronic device 100.

In other aspect, conversation can be sent to server when the electronicdevice 100 is idle. The operations of the contextual category detector130 and the response predictor 140 can be done in server (remotelylocated). Further, the LM training is performed when the predictionresponse are shared with the electronic device 100.

In an embodiment of the present disclosure, the electronic device 100may be in communication with a remote computing device (not shown) viaone or more communication networks. A communication network may be alocal area network (LAN), a wide area network (WAN), a mobile orcellular communication network, an extranet, an intranet, the Internetand/or the like. In an embodiment of the present disclosure, thecommunication network may provide communication capability between theremote computing device and the electronic device 100.

In an embodiment of the present disclosure, the remote computing devicemay be a cloud computing device or a networked server located remotelyfrom the electronic device 100. The remote computing device may includesimilar or substantially similar hardware elements to that of theelectronic device 100.

The FIG. 4 shows exemplary hardware elements of the electronic devicebut it is to be understood that other embodiments are not limitedthereon. In other embodiments, the electronic device 100 may includeless or more number of hardware elements. Further, the labels or namesof the hardware elements are used only for illustrative purpose and doesnot limit the scope of the invention. One or more hardware elements canbe combined together to perform same or substantially similar functionin the electronic device 100.

FIG. 5 is an overview illustrating communication among various hardwareelements of the electronic device for automatically predicting theresponse, according to an embodiment of the present disclosure.

Referring to FIG. 5, the information manager 120 can be configured toreceive the at least one input for example, the at least one message,the written communication, the topic selected from the writtencommunication, the topic formed based at least one input field availablein the application, the topic formed based at least one input fieldavailable in the applications, received mail, completeconversation/chat, or the like. The information manager 120 can beconfigured to communicate the received input with the contextualcategory detector 130.

In an embodiment of the present disclosure, the contextual categorydetector 130 can include a statistical modelling manager 132, a semanticmodelling manager 134, and a contextual modelling manager 136.

The statistical modelling manager 132 can be configured to identify oneor more statistical features associated with the received input. Forexample, the one or more statistical features can include time bound,location, etc.

The semantic modeling manager 134 can be configured to identify one ormore words associated with the received input. Further, the semanticmodelling manager 134 can be configured to identify one or morecategories of the received input. The one or more categories can beidentified by selecting one or more features associated with thereceived input. For example, the one or more features may include thecontext of the electronic device 100 and user of the electronic device100, a domain identification, a Dialog Act (DA) identification, asubject identification, a topic identification, a sentiment analysis, apoint of view (PoV), a user tracker information, and the like.

For example, the domain identification can include a time, a distance, atime-duration, a time-tense, a quantity, a relation, a location, and anobject/person, and the like. For example, the DA identification caninclude statement non-opinion, an acknowledgement, apology,agree/accept, appreciation, Yes-No-question, Yes-No-answers,conventional closing, WH-questions, No answers, reject, OR clause, downplayer, thanking, or the like.

For example, the sentiment analysis can include positive, neutral, andnegative. For example, the user tracker information can include usercontext, user tone (formal/informal). For example, the subjectidentification can include subject of the at least one message.

The contextual modelling manager 136 can be configured to identify theuser personalization information with respect to any of the applicationof the electronic device 100, and application context in order to extendthe context of the first application in the second application. Forexample, the contextual modelling manager 136 can be configured tocreate a time bound event from the at least one received message “meetsuzzane” from the messaging application (i.e., the first application).Thus, whenever the user of the electronic device 100 detects a “calendarapplication” (i.e., second application) for setting a reminder then thetime bound event is extended to the calendar application. If theelectronic device 100 detects the input “meet” in the UI of the calendarapplication, then the text “Suzzane” is automatically predicted anddisplayed to the user of the electronic device 100.

Further, the contextual category detector 130 can be configured tocommunicate with the response predictor 140. The response predictor 140include a language model 142 (hereinafter used as LM 142). The LM 142can be configured to include Language Model (LM) entries defined basedon the contextual category of the received input. For example, if thecontextual category of the received input is of type “PoV” i.e., “How doI look in in blue color shirt” then the predicted response can include,for example, text/words related to the PoV such as, “ this color issuits you”, “ blue color is too dark”, “look good in blue color”, andthe like.

The response predictor 140 can communicate with one or more languagemodel (LM) databases i.e., a preload LM 502, a user LM 504, and a timebound LM 506. The one or more LM databases can be configured to storethe one or more LM entries. The response predictor 140 can be configuredto retrieve the stored LM entries from the one or more LM databases. Theone or more LM databases can be communicatively coupled to the storage150 illustrated in FIG. 4.

In another embodiment, the one or more LM databases can be remotelylocated to the electronic device 100 and can be accessed through the oneor more communication networks.

The preload LM 502 can include the statistical LM entries trained withplethora of corpus (user inputs) along with the semantic understanding.The user LM 504 can be dynamically created on the electronic device 100and is trained based on the user activates (i.e., text, and graphicalelement(s) frequently used/accessed by the user in the event ofresponding/composing the message). Thus, the LM 142 can include aseparate contextual category i.e., “X” component (shown below inTable.1) along with each unigram, bigram and trigram entries. Forexample, the LM 142 can be (N+X)/(NN+X), where N=N gram and NN=Neuralnet.

TABLE 1 Language Model (LM) Category LM entry Feature “X” componentFrequency

For example, the “X” component can include “X1—domain identificationcomponent”, “X2—DA identification component”, “X3—sentimental analysiscomponent”, “X4—PoV component”, “X5—user tracker component” . . . Xn.The “X”. In an embodiment of the present disclosure, the “X” componentcan be updated by training the corpus for preload LM 502. Further, the“X” component can be updated by learning user's activities (Sentence(s)being typed, chat, conversation, emails and the like).

For example, in the conventional methods and systems, the user-1 of theelectronic device 100 receives the message “I am really sorry” from theuser-2 of the electronic device (not shown). In the course of respondingto the received message, the user-1 starts typing “No need to a . . . ,”according conventional methods and systems, all the features based onthe user typed text are extracted, the features such as, for e.g., aunigram feature (e.g., “a”), a bigram feature (“to a”), and a trigramfeatures (“need to a”). Further, Table. 2, below includes additionalextracted based on the text typed by the user.

TABLE 2 Text extraction Token Feature Probability Alright UNI ApocalypseUNI apologize UNI to_apologize BI to_appear BI need_to_apologize TRIneed_to_appear TRI

Unlike to conventional methods and systems, the proposed method can beused to provide the predictions based on the contextual category of thereceived message from the user-2. The proposed contextual categorydetector 130 can be configured to identify the contextual category ofthe received message i.e., the message “I am really sorry” is of type“APOLOGY”. Hence, LM entries corresponding to the contextual category‘APOLOGY’ along with the unigram, the bigram, the trigram features areretrieved and displayed to the user of the electronic device 100 (asshown in Table. 3)

TABLE 3 LM entries Token Feature DA Domain Probability Alright UNIAPOLOGY apologize UNI APOLOGY to_apologize BI APOLOGY need_to_apologizeTRI APOLOGY

Further, the contextual category detector 130 can be configured to trackthe user-1 activities (response to the message sent by the user of theelectronic device 100, predictions selected by the user of theelectronic device 100, or the like), and alter (e.g., train, update,modify, etc.) the LM 142 based on the user activities.

FIGS. 6A and 6B illustrates the UI for predicting subsequent meaningfulprediction during composing of the response to the received message,according to embodiments disclosed herein.

For example, consider a scenario in which the user of the electronicdevice 100 receives a message 600 i.e., “I am really sorry” from one ormore participant 602. The user of the electronic device 100 may intendto respond to the received message and starts typing/composing the texti.e., “No need to a . . . ” Accordingly, the proposed method can be usedto automatically predict the subsequent text/word of the in the sentencebeing composed (i.e., next meaningful word).

In order to predict the subsequent text/word of the in the sentencebeing composed, the information manager 120 can be configured tocommunicate the received message (“I am really sorry”) with thecontextual category detector 130 illustrated in FIG. 5. The contextualcategory detector 130 can be configured to identify the at least onecontextual category of the received message i.e., the contextualcategory is of type “Apology”. Further, the contextual category detector130 can communicate with the response predictor 140 to retrieve the LMentries based on the contextual category “Apology”.

The LM 142 can be configured to identify the at least one feature (i.e.,unigram, bigram and trigram) from the text input provided, by the userof the electronic device 100, during the response. Thus, the LM 142 canbe configured to retrieve the LM entries based on the at least onefeature along with the contextual category “Apology” of the receivedmessage 600. Hence, the response predictor 140 can be configured toretrieve and display at least one subsequent response 604 i.e.,“apologize”, “apology”, “be apologetic”, or the like, from the LM 142based on the contextual category of the received message 600. Hence, forexample, sentence 606 being composed can be No need to “apologize” (asillustrated in FIG. 6B)

FIG. 7 is a flow diagram illustrating a method for predicting theresponse, according to an embodiment of the present disclosure.

Referring to FIG. 7, at operation 702, the electronic device 100 mayreceive the at least one message. For example, in the electronic device100 as illustrated in FIG. 4, the information manager 120 can beconfigured to receive the at least one message.

At operation 704, the electronic device 100 identifies the at least onecontextual category of the at least one message. For example, in theelectronic device 100 as illustrated in FIG. 4, the contextual categorydetector 130 can be configured to identify the at least one contextualcategory of the at least one message.

At operation 706, the electronic device 100 predicts the at least oneresponse for the at least one message from the LM 142 based on the atleast one contextual category. For example, in the electronic device 100as illustrated in FIG. 4, the response predictor 140 can be configuredto predict the at least one response for the at least one message fromthe LM 142 based on the at least one contextual category.

At operation 708, the electronic device 100 prioritizes the at least onepredicted response. For example, in the electronic device 100 asillustrated in FIG. 4, the response predictor 140 can be configured toprioritize the at least one predicted response.

At operation 710, the electronic device 100 causes to display the atleast one predicted response on the screen. For example, in theelectronic device 100 as illustrated in FIG. 4, the response predictor140 can be configured to cause to display the at least one predictedresponse on the screen.

At operation 712, the electronic device 100 tracks the user activities.For example, in the electronic device 100 as illustrated in FIG. 4, thecontextual category detector 130 can be configured to track the useractivities.

At operation 714, the electronic device 100 trains the LM 142. Forexample, in the electronic device 100 as illustrated in FIG. 4, theresponse predictor 140 can be configured to train the LM 142.

The various actions, acts, blocks, steps, etc., as illustrated in FIG. 7may be performed in the order presented, in a different order, orsimultaneously. Further, in some embodiments, some of the actions, acts,blocks, steps, etc., may be omitted, added, modified, skipped, etc.,without departing from the scope of the disclosure.

FIG. 8 illustrates a UI for responding to the message using the at leastone predicted response, according to an embodiment of the presentdisclosure.

Referring to FIG. 8, the electronic device 100 may have a messagetranscript 800 showing the conversation between the user of theelectronic device 100 and one or more participants, such as participant804. The message transcript 800 may include a message 802, received from(an electronic device used by) the participant 804.

The content of the message 802 includes “Hey I got my results. I am thetopper!” In an embodiment of the present disclosure, the proposed methodcan be used to determine at least one contextual category of the message802 i.e., the contextual category of the received message 802 can be fore.g., “Appreciation”. Further, the proposed method can be used topredict at least one response 806 i.e., “guessed it “am happy for you”“congrats”, or the like from the LM 142 from the contextual category LM156.

FIG. 9A is a step by step illustration for predicting response for aselected message from the plurality of messages, according to anembodiment of the present disclosure.

At operation 910 a, the electronic device 100 may receive the at leastone message. For example, in the electronic device 100 as illustrated inthe FIG. 4, the information manager 120 can be configured to receive theat least one message.

For example, referring to the UI, the display 170 can be configured todetect an input 902 a (i.e., tap, gesture, or the like) on at least onemessage 904 a (“You had an exam yesterday”) from the plurality ofmessages.

At operation 912 a, the electronic device 100 may recapture one or morewords. For example, in the electronic device 100 as illustrated in FIG.4, the contextual category detector 130 can be configured to recaptureone or more words.

At operation 914 a, the electronic device 100 may identify at least onecontextual category of the selected words. For example, in theelectronic device 100 as illustrated in FIG. 4, the contextual categorydetector 130 can be configured to identify the at least one contextualcategory of the selected words.

At operation 916 a, the electronic device 100 may use the contextualcategory along with the LM 142. For example, in the electronic device100 as illustrated in FIG. 4, the response predictor 140 can beconfigured to use the contextual category along with the LM 142.

At operation 918 a, the electronic device 100 may compute values (i.e.,LM entries) from the LM 142. For example, in the electronic device 100as illustrated in FIG. 4, the response predictor 140 can be configuredto compute values from the LM 142.

At operation 920 a, the electronic device 100 may retrieve the responsepredictions and next word predictions. For example, in the electronicdevice 100 as illustrated in FIG. 4, the response predictor 140 can beconfigured to retrieve the response predictions and next wordpredictions.

Thus, based on the user input 902 a on the at least one message 904 a,the response predictor 140 can be configured to dynamically update anddisplay the response predictions and next word predictions 906 a i.e.,“It was”, “Exam was”, or the like.

FIG. 9B is a step by step illustration for predicting response based fora selected input topic, according to an embodiment of the presentdisclosure.

At operation 910 b, the electronic device 100 may receive the at leastinput topic. For example, in the electronic device 100 as illustrated inFIG. 4, the information manager 120 can be configured to receive the atleast input topic.

For example, referring to the UI, the display 170 can be configured todetect the input 902 b (i.e., tap, gesture, or the like) on the inputtopic 904 b (i.e., at least one word/text selected from the composingtext).

At operation 912 b, the electronic device 100 may recapture the one ormore words. For example, in the electronic device 100 as illustrated inFIG. 4, the contextual category detector 130 can be configured torecapture the one or more words.

At operation 914 b, the electronic device 100 may identify the at leastone contextual category of the selected words. For example, in theelectronic device 100 as illustrated in FIG. 4, the contextual categorydetector 130 can be configured to identify the at least one contextualcategory of the selected words.

At operation 916 b, the electronic device 100 may use the contextualcategory along with the LM 142. For example, in the electronic device100 as illustrated in FIG. 4, the response predictor 140 can beconfigured to use the contextual category along with the LM 142.

At operation 918 b, the electronic device 100 may compute values fromthe LM 142. For example, in the electronic device 100 as illustrated inFIG. 4, the response predictor 140 can be configured to compute thevalues from the LM 142.

At operation 920 b, the electronic device 100 may retrieve themeaningful predictions based on the composing text selection. Forexample, in the electronic device 100 as illustrated in FIG. 4, theresponse predictor 140 can be configured to retrieve (or, predict) themeaningful predictions based on the composing text selection.

Thus, based on the user input 902 b on the at least one composing text,the response predictor 140 can be configured to dynamically update anddisplay the meaningful predictions 906 b (i.e., “It papers”,“Questions”, “answers”, or the like) based on the composing textselection.

FIG. 10 is a flow diagram illustrating a method for predicting theresponse based on the statistical modelling manager, accordingembodiments as disclosed herein.

Referring to FIG. 10, at operation 1002, the electronic device 100 mayreceive the input topic from the first application. For example, in theelectronic device 100 as illustrated in FIG. 4, the information manager120 can be configured to receive the input topic from the firstapplication.

At operation 1004, the electronic device 100 may identify the at leastone contextual event associated with the second application. Forexample, in the electronic device 100 as illustrated in FIG. 4, thecontextual category detector 130 can be configured to identify the atleast one contextual event associated with the second application.

At operation 1006, the electronic device 100 may predict the at leastone response for the at least one input topic from the first applicationbased on the at least one contextual event. For example, in theelectronic device 100 as illustrated in FIG. 4, the response predictor140 can be configured to predict the at least one response for the atleast one input topic from the first application based on the at leastone contextual event.

At operation 1008, the electronic device 100 may computedynamic-interpolation-weights (λ1, λ2, λ3) of each LM databases (i.e.,the preload LM 502, the user LM 504, the time bound LM 506). Thedynamic-interpolation-weights can be used to prioritize words among theLM databases.

At operation 1010, the electronic device 100 may find probabilities(P_(PLM), P_(ULM), P_(TLM)) of ‘WORD’ from each of the LM databases(i.e., the preload LM 502, the user LM 504, the time bound LM 506respectively).

At operation 1012, the electronic device 100 may calculate Pc (combinedprobability) for each of the word(s) retrieved from each of the each ofthe LM databases (i.e., LM models) and prioritize the predictions basedon the Pc (or, based on the parameters such as Relevancy, sort by recentand so on). For example, in the electronic device 100 as illustrated inFIG. 4, the response predictor 140 can be configured to calculates thePc (combined probability) for each of the word(s) retrieved from each ofthe each of the LM databases (i.e., LM models).

At operation 1014, the electronic device 100 may cause to display the atleast one predicted response on the screen. For example, in theelectronic device 100 as illustrated in FIG. 4, the response predictor140 can be configured to cause to display the at least one predictedresponse on the screen.

At operation 1016, the electronic device 100 may track the useractivities. For example, in the electronic device 100 as illustrated inFIG. 4, the response predictor 140 can be configured to track the useractivities.

At operation 1018, the electronic device 100 may train the LM 142. Forexample, in the electronic device 100 as illustrated in FIG. 4, theresponse predictor 140 can be configured to train the LM 142 based onthe user activities and LM entries retrieved from each of the LMdatabase.

FIG. 11 is a waveform for computing dynamic interpolation weights withtime bound, according to an embodiment of the present disclosure.

Table. 4 (shown below) tabulates the dynamic interpolation weights foreach of the LM database with time bound LM and without time bound LM.

TABLE 4 LM database Interpolation weights λ1 λ2 λ3 Σ λ_(i) Without TimeBound LM 0.7 0.3 0 1 With Time Bound LM (1 − y) * 0.7 (1 − y) * 0.3 Y 1

The electronic device 100 can be configured to estimate interpolationweight (λ3) with the time bound LM using the equation (7).

$\begin{matrix}{y = \left\{ \begin{matrix}{{{{- m_{2}}t^{2}} + \gamma_{{TB}\; \max}};} & {0 < t<=T_{O}} & ({Parabolic}) \\{{{{- m_{2}}t} + {m_{2}T_{TB}}};} & {T_{O} < t < T_{TB}} & ({Linear})\end{matrix} \right.} & (7)\end{matrix}$

where m_(1 and) m₂=rate of change of interpolation weight with respectto time;

y_(TBmax)=maximum interpolation weight for Time Bound LM

T_(TB)=Time limit for Time Bound LM

T_(O)=Value that lies between 0 and T_(TB)

T_(O)∈[0, T_(TB)]

$\begin{matrix}{y = \left\{ \begin{matrix}{{{{- \left( {y_{TBmax}\text{/}T_{TB}} \right)}*t} + y_{TBmax}};} & \left( {T_{O==}0} \right) \\{{{{- \left( {y_{TBmax}\text{/}T_{TB}^{2}} \right)}*t^{2}} + y_{TBmax}};} & \left( {T_{O==}T_{TB}} \right)\end{matrix} \right.} & (8)\end{matrix}$

FIGS. 12A and 12B illustrate a UI in which the contextual event from thereceived message is identified and extended from first application tosecond application, according to an embodiment of the presentdisclosure.

Referring to FIG. 12A, the electronic device 100 may have a messagetranscript 1200 showing the conversation between the user of theelectronic device 100 and one or more participants, such as participant1206. The message transcript 1200 may include a message 1202 receivedfrom the participant 1206 and message 1204 sent by the user of theelectronic device 100.

The contextual category detector 130 illustrated in FIG. 4 can beconfigured to identify the contextual event (i.e., fixed time bound,semantic time bound, and contextual time bound) associated with themessage 1202 and the message 1204. The message 1204 includes “Great!!Try to Meet Suzanne!” The LM entries during the fixed time bound aremanaged via parabolic/linear on time e.g., reduce priority/frequencyover time. The LM entries during semantic time bound may not be usefulafter trip and thereby the LM 142 may delete the entry by understandingthe message. The contextual time bound is more useful in thecommunication relate applications and prioritizes entry based on theapplication context.

Referring to FIG. 12B, the user of the electronic device 100 may launchthe calendar application for setting a reminder. When the user of theelectronic device 100 composes, using the keypad, a text 1208 “meet” inan input tab of the calendar application 1210, then the next response1212 “Suzzane” can be automatically predicted and displayed on thescreen (e.g., in the text prediction tab of the keypad., default areadefined by OEM, default area defined by the user, etc.,) of theelectronic device 100.

Accordingly, the proposed method of the present disclosure can be usedto provide the meaningful predictions. The proposed method can be usedto extend contextual event of the messaging application and providepredictions in the application.

FIGS. 13A and 13B illustrate another UI in which the contextual eventfrom the received message is identified and extended from firstapplication to second application, according to an embodiment of thepresent disclosure.

Referring to FIG. 13A, the electronic device 100 may have a messagetranscript 1300 showing the message received from one or moreparticipants. The message transcript 1300 may include the message 1302received from the participant.

The contextual category detector 130 can be configured to identify thecontextual event (i.e., fixed time bound, semantic time bound, andcontextual time bound) associated with the message 1302. The message1302 includes “Buy Tropicana orange, cut mango and milk when you comehome”.

Referring to FIG. 13B, the user of the electronic device 100 may launch(access/open) a shopping application (i.e., related application to thatof the contextual event). When the user of the electronic device 100composes, using the keypad, at least one text 1304 (“Tropicana”) fromthe message 1302 in the input tab of the shopping application, then thenext word(s) 1306 “Orange”, “cut mango”, Milk, or the like can beautomatically predicted and displayed on the screen (e.g., in the textprediction tab of the keypad., default area defined by the OEM, defaultarea defined by the user, etc.,) of the electronic device 100.

FIG. 14A illustrates an exemplary UI in which a contextual relatedapplication based on the received message is predicted and displayed onthe screen of the electronic device, according to an embodiment of thepresent disclosure.

Referring to FIG. 14A, the user of the electronic device 100 may receivea message 1402 a from one or more participants. The contextual categorydetector 130 can be configured to detect the at least contextual event(i.e., contextual time bound event) associated with the message 1402 a.Thus, based on the contextual time bound event, the response predictor140 can be configured to predict and display the at least one contextualrelated application.

As illustrated in FIG. 14A, based on the contextual time bound event arelated application i.e., a graphical icon 1404 a of the calendarapplication can be predicted and displayed on the screen of theelectronic device 100.

FIG. 14B illustrates a UI in which the predicted response for themessage is displayed with in the notification area of the electronicdevice, according to an embodiment of the present disclosure.

Referring to FIG. 14B, the electronic device 100 may receive a message1402 b from one or more participants. The at least one predictedresponse 1404 b for the message 1402 b is automatically predicted anddisplayed within the notification area of the electronic device 100.

Unlike to conventional methods and systems, the proposed method can beused to provide the response predictions for the message(s) receivedwithout launching the message application.

FIG. 15 illustrates a UI in which multiple response messages arepredicted based on contextual grouping of the related messages,according to an embodiment of the present disclosure.

Referring to FIG. 15, the user of the electronic device 100 may receivemessages 1502 and 1504 from a participant 1506, and a message 1508 froma participant 1510. The contextual category detector 130 can beconfigured to identify one or more contextual category of the messages1502 (i.e., “You had an exam yesterday) and 1504 (“how was it”).Further, based on the one or more contextual category (i.e., both themessages 1502 and 1504 are received from same participant 1506, contentavailable in both the messages 1502 and 1504 are contextually related,and the like) the response predictor 140 can be configured to predictone or more response messages and group 1512 the one or more predictedresponses. Similarly, based on the one or more contextual category(i.e., of the message 1508, content available in the message 1508, orthe like) the response predictor 140 can be configured to predict one ormore response messages and group 1514 the one or more predictedresponses.

Unlike to conventional methods and systems, the proposed method can beused to provide the response prediction by considering individual orgroup conversations, one or more queries from one or more participantsare addressed, one or more queries from the user of the electronicdevice 100, and the like.

FIGS. 16A to 16B illustrates a longer pattern scenario in which themeaningful response (next suggested word) is predicted in the longerpattern sentence, according to an embodiment of the present disclosure.

Referring to FIG. 16A, the electronic device 100 detects the input topici.e., composed text of longer sentence pattern i.e., “the sky above ourhead is . . . ” Unlike to conventional methods and systems, the proposedcontext category manager 130 can be configured to analyze the receivedinput topic and identify the at least one contextual category of thereceived input topic. Thus, based on the at least one content “The Sky”available in the input topic, the response predictor 140 can beconfigured to predict and display the response (next word) “Blue”.

Thus, the LM 142 utilizes the contextual input class (to include longerpattern) along with N Gram (Tri gram) language model. Unlike toconventional methods and systems, the proposed method can provide theresponse predictions by considering only selective inputs (“the sky”)and not the whole longer pattern.

Similarly, referring to FIG. 16B, the selective input (“party”) isconsidered and according the response “Friday night” is predicted anddisplayed on the screen of the electronic device 100.

FIG. 17 is a flow diagram illustrating a method for predicting theresponse by understanding input views rendered on the screen of theelectronic device, according to an embodiment of the present disclosure.

At operation 1702, the electronic device 100 may parse the informationrendered on the screen (screen reading). For example, in the electronicdevice 100 as illustrated in FIG. 4, the contextual category detector130 can be configured to parse the information rendered on the screen(screen reading).

At operation 1704, the electronic device 100 may extract the text (i.e.,hint, label, or the like) in response to parsing the screen. Forexample, in the electronic device 100 as illustrated in FIG. 4, thecontextual category detector 130 can be configured to extract the textin response to parsing the screen.

At operation 1706, the electronic device 100 may map the extracted textwith the input views. For example, in the electronic device 100 asillustrated in FIG. 4, the contextual category detector 130 can beconfigured to map the extracted text with the input views.

At operation 1708, the electronic device 100 may perform a semanticbased modelling. Further, at operation 1710, the electronic device 100prioritize the predictions.

FIGS. 18A to 18C is a UI displaying at least one predicted response byunderstanding input views rendered on the screen of the electronicdevice, according to an embodiment of the present disclosure.

The electronic device 100 can be configured to parse the informationrendered on the screen i.e., identifying the text rendered on thescreen. The texts (i.e., input views, hints, labels, or the like) on thescreen such as “your name”, “Your email address”, “password”, “enterpassword”, “enter email”, or the like, are parsed and provided to thecontextual category detector 130 as illustrated in FIG. 4. Thecontextual category detector 130 can be configured to identify thecontextual category of the text parsed i.e., “Your name” is of category“subject”, “you phone number” is of category “ contacts”, etc.,identified from the contextual LM database. Further, the predictiondetector 140 illustrated in FIG. 4 can be configured to display theresponse predictions based on the input views/input text field inaccordance with the at least one category determined. The responsepredictions for the input text filed “Your name” can be “steph”,“curry”, or the like.

FIGS. 19A to 19C illustrates a UI displaying multiple predicted responsebased on at least one event associated with at least one participant,according to an embodiment of the present disclosure.

For example, consider a scenario in which the user of the electronicdevice 100 may receive at least one message 1902 from the at least oneparticipant 1904. Unlike to conventional methods and systems, theproposed contextual category detector 130 can be used to identify atleast one event (e.g., birthday event, anniversary event, etc.)associated with the participant 1904. The at least one event can beautomatically retrieved from the at least one application (e.g.,calendar application, SNS application, etc.,) associated with theelectronic device 100.

Hence, based on the least one event the response predictor 140 can beconfigured to predict, prioritize and display multiple responses. Forexample, if the content of the message 1902 includes “shall we go formovie?”, then the response predictor 140 can be configured to providethe response predictions 1906 i.e., “Sure, we should definitely go”,“movie will be good”, Sure. Further, the response predictions 1906 caninclude the response predicted based on the event detection i.e., “Happybirthday buddy.”

FIGS. 20A to 20C illustrates a UI displaying predicted response based onthe context associated with the user and the electronic device,according to an embodiment of the present disclosure.

For example, consider a scenario in which the user of the electronicdevice 100 may receive at least one message 2002 from the at least oneparticipant 2004. Unlike to conventional methods and systems, theproposed contextual category detector 130 can be used to identify thecontext (i.e., location, weather condition, etc.,) of the electronicdevice 100 a user context (i.e., appointment, user tone, reminder,etc.).

Hence, based on the context of the electronic device 100 the responsepredictor 140 can be configured to predict, prioritize and displaymultiple responses. For example, if the content of the message 2002includes “How about trip to Goa this December?”, then the responsepredictor 140 can be configured to provide the response predictions 2006i.e., “Wow! Let's do it”. Further, the response predictions 2006 caninclude the response predicted based on the context (weather forecastprovided by weather application, or weather forecast provided by anyother means) of the electronic device 100 i.e., “it will be completelyraining.”

Further, if the content of message 2008 includes “party is at 3. Whenwill you reach here?”, then based on the context (e.g., location, time,or the like) of the electronic device 100 the response predictor 140 canbe configured to predict, prioritize and display multiple responses 2010i.e., “will reach in one hour”, “In another”, 2:45 PM”, or the like.

Furthermore, if the content of message 2012 includes “I get frequentheadache now a days” and content of message 2014 includes “Will visitdoctor tomo!”, then based on the context (e.g., time) of the electronicdevice 100 and context of at least one participant/user (i.e.,Appointment, health, user tone, or the like) the response predictor 140can be configured to predict, prioritize and display multiple responses2016 i.e., “How are you feeling now?”, “Did you visit the doctor”, orthe like.

FIGS. 21A to 21D illustrate various table tabulating the responsepredictions and next suggestive words for different samples of inputs,according to an embodiment of the present disclosure.

The electronic device 100 or method (for example, operations) accordingto various embodiments may be performed by at least one computer (forexample, a processor 160) which executes instructions included in atleast one program from among programs which are maintained in acomputer-readable storage medium.

When the instructions are executed by a computer (for example, theprocessor 160), the at least one computer may perform a functioncorresponding to the instructions. In this case, the computer-readablestorage medium may be the memory, for example.

Certain aspects of the present disclosure can also be embodied ascomputer readable code on a non-transitory computer readable recordingmedium. A non-transitory computer readable recording medium is any datastorage device that can store data which can be thereafter read by acomputer system. Examples of the non-transitory computer readablerecording medium include a Read-Only Memory (ROM), a Random-AccessMemory (RAM), Compact Disc-ROMs (CD-ROMs), magnetic tapes, floppy disks,and optical data storage devices. The non-transitory computer readablerecording medium can also be distributed over network coupled computersystems so that the computer readable code is stored and executed in adistributed fashion. In addition, functional programs, code, and codesegments for accomplishing the present disclosure can be easilyconstrued by programmers skilled in the art to which the presentdisclosure pertains.

At this point it should be noted that the various embodiments of thepresent disclosure as described above typically involve the processingof input data and the generation of output data to some extent. Thisinput data processing and output data generation may be implemented inhardware or software in combination with hardware. For example, specificelectronic components may be employed in a mobile device or similar orrelated circuitry for implementing the functions associated with thevarious embodiments of the present disclosure as described above.Alternatively, one or more processors operating in accordance withstored instructions may implement the functions associated with thevarious embodiments of the present disclosure as described above. Ifsuch is the case, it is within the scope of the present disclosure thatsuch instructions may be stored on one or more non-transitory processorreadable mediums. Examples of the processor readable mediums include aROM, a RAM, CD-ROMs, magnetic tapes, floppy disks, and optical datastorage devices. The processor readable mediums can also be distributedover network coupled computer systems so that the instructions arestored and executed in a distributed fashion. In addition, functionalcomputer programs, instructions, and instruction segments foraccomplishing the present disclosure can be easily construed byprogrammers skilled in the art to which the present disclosure pertains.

The instructions may include machine language codes created by acompiler, and high-level language codes that can be executed by acomputer by using an interpreter. The above-described hardware devicemay be configured to operate as one or more software modules to performthe operations according to various embodiments of the presentdisclosure, and vice versa.

While the present disclosure has been shown and described with referenceto various embodiments thereof, it will be understood by those skilledin the art that various changes in form and details may be made thereinwithout departing from the spirit and scope of the present disclosure asdefined by the appended claims and their equivalents.

What is claimed is:
 1. An electronic device for automatically predicting a response, the electronic device comprising: a display; and a processor configured to: receive at least one message; identify at least one contextual category of the at least one message; and predict at least one response for the at least one message from a language model based on the at least one contextual category, and control the display to display the at least one predicted response.
 2. The electronic device of claim 1, wherein the contextual category of the at least one message is automatically identified based on at least one context indicative.
 3. The electronic device of claim 2, wherein the at least one context indicative is determined based on at least one of content available in the at least one message, user activities, events defined in the electronic device, a user associated with the at least one message, a user context of the electronic device, and a context of the electronic device.
 4. The electronic device of claim 3, wherein the context of the electronic device is determined based on sensing data sensed by at least one sensor of the electronic device.
 5. The electronic device claim 1, wherein the at least one message comprises one of a topic selected from a written communication and a topic formed based at least one input filled available in an application.
 6. The electronic device of claim 5, wherein, when the received at least one message is an input topic from a first application, the processor further configured to: identify at least one contextual event associated with a second application, and predict at least one response for the at least one input topic from the first application based on at least one contextual event.
 7. The electronic device of claim 6, wherein the at least one contextual event is a fixed time bound, a semantic time bound, and a contextual time bound.
 8. The electronic device of claim 6, wherein the at least one contextual event associated with the second application is determined based on at least one context indicative associated with the input topic of the first application, and wherein the at least one context indicative is determined based on at least one of content available in the input topic, context of the first application, user activities, and events defined.
 9. The electronic device of claim 1, wherein the at least one message and the at least one predicted response are displayed within the notification area.
 10. The electronic device of claim 1, wherein the at least one response for the at least one message is predicted in response to an input on the at least one received message.
 11. A method for automatically predicting a response, the method comprising: receiving, by an information manager, at least one message at an electronic device; identifying, by a contextual category detector, at least one contextual category of the at least one message; predicting, by a response predictor, at least one response for the at least one message from a language model based on the at least one contextual category; and causing, by the response predictor, to display the at least one predicted response on a screen of an electronic device.
 12. The method of claim 11, wherein the contextual category of the at least one message is automatically identified based on at least one context indicative.
 13. The method of claim 12, wherein the at least one context indicative is determined based on at least one of content available in the at least one message, user activities, events defined in the electronic device, a user associated with the at least one message, a user context of the electronic device, and a context of the electronic device.
 14. The method of claim 11, wherein the context of the electronic device is determined based on sensing data sensed by at least one sensor of the electronic device.
 15. The method of claim 11, wherein the at least one message comprises one of a topic selected from a written communication and a topic formed based at least one input filled available in an application.
 16. The method of claim 11, wherein, when the received at least one message is an input topic from a first application, the identifying further comprises identifying at least one contextual event associated with a second application, and the predicting further comprises predicting at least one response for the at least one input topic from the first application based on at least one contextual event.
 17. The method of claim 16, wherein the at least one contextual event is a fixed time bound, a semantic time bound, and a contextual time bound.
 18. The method of claim 16, wherein the at least one contextual event associated with the second application is determined based on at least one context indicative associated with the input topic of the first application, and wherein the at least one context indicative is determined based on at least one of content available in the input topic, context of the first application, user activities, and events defined.
 19. The method of claim 11, wherein the at least one message and the at least one predicted response are displayed within the notification area of the electronic device.
 20. The method of claim 11, wherein the at least one response for the at least one message is predicted in response to an input on the at least one received message. 